In machine learning, developing a classifier can involve training and testing the classifier on labeled cases. Testing the classifier can be performed to determine an estimate of the generalization error of the classifier. The generalization error indicates how well a classifier is expected to perform on cases unseen during training of the classifier. However, obtaining labeled cases can be an expensive, time-consuming, and difficult task.